Adaptive algorithms in accelerometer biometrics

Nowadays, many services are available from mobile devices, like smartphones. A growing number of people are using these devices to access bank accounts, social networks and to store personal information. However, common authentication mechanisms already present in these devices may not provide enough security. Recently, a new authentication method, named accelerometer biometrics, has been proposed. This method allows the identification of users using accelerometer data. Accelerometers, usually present in modern smartphones, are devices that measure acceleration forces. In accelerometer biometrics, a model is induced for the user of the smartphone. However, as a behavioral biometric technology, user models may became outdated over time. This paper investigates the use of adaptation mechanisms to update biometric user models induced by accelerometer data along the time. The paper also proposes and evaluates a new adaptation mechanism with promising experimental results.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Impact of injection attacks on sensor-based continuous authentication for smartphones

Given the relevance of smartphones for accessing personalized services in smart cities, Continuous Authentication (CA) mechanisms are attracting attention to avoid impersonation attacks. Some of them leverage Data Stream Mining (DSM) techniques applied over sensorial information. Injection attacks can undermine the effectiveness of DSM-based CA by fabricating artificial sensorial readings. The goal of this paper is to study the impact of injection attacks in terms of accuracy and immediacy to illustrate the time the adversary remains unnoticed. Two well-known DSM techniques (K-Nearest Neighbours and Hoeffding Adaptive Trees) and three data sources (location, gyroscope and accelerometer) are considered due to their widespread usage Results show that even if the attacker does not previously know anything about the victim, a significant attack surface arises - 1.35 min are needed, in the best case, to detect the attack on gyroscope and accelerometer and 7.27 min on location data. Moreover, we show that the type of sensor at stake and configuration settings may have a dramatic effect on countering this threat.This work was supported by the Spanish Ministry of Science, Innovation and Universities grants TIN2016-79095-C2-2-R (SMOG-DEV), PID2019-111429RBC21(ODIO); by Comunidad de Madrid (CAM) grant P2018/TCS4566 (CYNAMON-CM) funded with European FEDER funds; and CAVTIONS-CM-UC3M funded by UC3M and CAM

Activity-Aware Electrocardiogram-based Passive Ongoing Biometric Verification

Identity fraud due to lost, stolen or shared information or tokens that represent an individual\u27s identity is becoming a growing security concern. Biometric recognition - the identification or verification of claimed identity, shows great potential in bridging some of the existing security gaps. It has been shown that the human Electrocardiogram (ECG) exhibits sufficiently unique patterns for use in biometric recognition. But it also exhibits significant variability due to stress or activity, and signal artifacts due to movement. In this thesis, we develop a novel activity-aware ECG-based biometric recognition scheme that can verify/identify under different activity conditions. From a pattern recognition standpoint, we develop algorithms for preprocessing, feature extraction and probabilistic classification. We pay particular attention to the applicability of the proposed scheme in ongoing biometric verification of claimed identity. Finally we propose a wearable prototype architecture of our scheme

Secure and Usable User-in-a-Context Continuous Authentication in Smartphones Leveraging Non-Assisted Sensors

Smartphones are equipped with a set of sensors that describe the environment (e.g., GPS, noise, etc.) and their current status and usage (e.g., battery consumption, accelerometer readings, etc.). Several works have already addressed how to leverage such data for user-in-a-context continuous authentication, i.e., determining if the porting user is the authorized one and resides in his regular physical environment. This can be useful for an early reaction against robbery or impersonation. However, most previous works depend on assisted sensors, i.e., they rely upon immutable elements (e.g., cell towers, satellites, magnetism), thus being ineffective in their absence. Moreover, they focus on accuracy aspects, neglecting usability ones. For this purpose, in this paper, we explore the use of four non-assisted sensors, namely battery, transmitted data, ambient light and noise. Our approach leverages data stream mining techniques and offers a tunable security-usability trade-off. We assess the accuracy, immediacy, usability and readiness of the proposal. Results on 50 users over 24 months show that battery readings alone achieve 97.05% of accuracy and 81.35% for audio, light and battery all together. Moreover, when usability is at stake, robbery is detected in 100 s for the case of battery and in 250 s when audio, light and battery are applied. Remarkably, these figures are obtained with moderate training and storage needs, thus making the approach suitable for current devices.This work has been partially supported by MINECO grants TIN2013-46469-R (SPINY), TIN2016-79095-C2-2-R (SMOG-DEV); CAM grant S2013/ICE-3095 (CIBERDINE), co-funded with European FEDER funds

From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare

<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p> <p> </p

A Supervised ML Biometric Continuous Authentication System for Industry 4.0

Continuous authentication (CA) is a promising approach to authenticate workers and avoid security breaches in the industry, especially in Industry 4.0, where most interaction between workers and devices takes place. However, introducing CA in industries raises the following unsolved questions regarding machine learning (ML) models: its precision and performance; its robustness; and the issue about if or when to retrain the models. To answer these questions, this article explores these issues with a proposed supervised versus nonsupervised ML-based CA system that uses sensors, applications statistics, or speaker data collected by the operator’s devices. Experiments show supervised models with equal error rates of 7.28% using sensors data, 9.29% with statistics, and 0.31% with voice, a significant improvement of 71.97, 62.14, and 97.08%, respectively, over unsupervised models. Voice is the most robust dimension when adding new workers, with less than 2% of false acceptance rate even if workforce size is doubled

Biometric walk recognizer. Research and results on wearable sensor-based gait recognition

Gait is a biometric trait that can allow user authentication, though being classified as a "soft" one due to a certain lack in permanence, and to sensibility to specific conditions. The earliest research relies on computer vision-based approaches, especially applied in video surveillance. More recently, the spread of wearable sensors, especially those embedded in mobile devices, which are able to capture the dynamics of the walking pattern through simpler 1D signals, has spurred a different research line. This capture modality can avoid some problems related to computer vision-based techniques, but suffers from specific limitations. Related research is still in a less advanced phase with respect to other biometric traits. However, the promising results achieved so far, the increasing accuracy of sensors, the ubiquitous presence of mobile devices, and the low cost of related techniques, make this biometrics attractive and suggest to continue the investigations in this field. The first Chapters of this thesis deal with an introduction to biometrics, and more specifically to gait trait. A comprehensive review of technologies, approaches and strategies exploited by gait recognition proposals in the state-of-the-art is also provided. After such introduction, the contributions of this work are presented in details. Summarizing, it improves preceding result achieved during my Master Degree in Computer Science course of Biometrics and extended in my following Master Degree Thesis. The research deals with different strategies, including preprocessing and recognition techniques, applied to the gait biometrics, in order to allow both an automatic recognition and an improvement of the system accuracy